Overview

Dataset statistics

Number of variables28
Number of observations42
Missing cells259
Missing cells (%)22.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory220.0 B

Variable types

Numeric25
DateTime1
Categorical1
Boolean1

Alerts

Bedtime is highly correlated with TST and 6 other fieldsHigh correlation
TST is highly correlated with Bedtime and 5 other fieldsHigh correlation
WASO is highly correlated with NOA and 3 other fieldsHigh correlation
Waketime is highly correlated with Bedtime and 2 other fieldsHigh correlation
TIB is highly correlated with Bedtime and 5 other fieldsHigh correlation
NOA is highly correlated with WASO and 1 other fieldsHigh correlation
LSD is highly correlated with Bedtime and 5 other fieldsHigh correlation
DSD is highly correlated with BS and 1 other fieldsHigh correlation
REMSD is highly correlated with SS and 2 other fieldsHigh correlation
SS is highly correlated with TST and 6 other fieldsHigh correlation
MS is highly correlated with REMSD and 2 other fieldsHigh correlation
BS is highly correlated with DSD and 1 other fieldsHigh correlation
SE is highly correlated with WASO and 1 other fieldsHigh correlation
SMI is highly correlated with WASO and 1 other fieldsHigh correlation
AI is highly correlated with WASO and 2 other fieldsHigh correlation
REMP is highly correlated with REMSD and 1 other fieldsHigh correlation
SWSP is highly correlated with LSD and 2 other fieldsHigh correlation
Onset is highly correlated with Bedtime and 5 other fieldsHigh correlation
Offset is highly correlated with Bedtime and 2 other fieldsHigh correlation
TSDP is highly correlated with Bedtime and 5 other fieldsHigh correlation
Midpoint is highly correlated with Waketime and 1 other fieldsHigh correlation
Bedtime is highly correlated with TST and 6 other fieldsHigh correlation
TST is highly correlated with Bedtime and 5 other fieldsHigh correlation
WASO is highly correlated with SE and 1 other fieldsHigh correlation
Waketime is highly correlated with Offset and 1 other fieldsHigh correlation
TIB is highly correlated with Bedtime and 5 other fieldsHigh correlation
NOA is highly correlated with AIHigh correlation
LSD is highly correlated with Bedtime and 5 other fieldsHigh correlation
DSD is highly correlated with BS and 1 other fieldsHigh correlation
REMSD is highly correlated with SS and 2 other fieldsHigh correlation
SS is highly correlated with Bedtime and 7 other fieldsHigh correlation
MS is highly correlated with REMSD and 2 other fieldsHigh correlation
BS is highly correlated with DSD and 1 other fieldsHigh correlation
SE is highly correlated with WASO and 2 other fieldsHigh correlation
SMI is highly correlated with WASO and 1 other fieldsHigh correlation
AI is highly correlated with NOAHigh correlation
REMP is highly correlated with REMSD and 1 other fieldsHigh correlation
SWSP is highly correlated with LSD and 2 other fieldsHigh correlation
Onset is highly correlated with Bedtime and 5 other fieldsHigh correlation
Offset is highly correlated with Waketime and 1 other fieldsHigh correlation
TSDP is highly correlated with Bedtime and 5 other fieldsHigh correlation
Midpoint is highly correlated with Waketime and 1 other fieldsHigh correlation
SleepRegularity is highly correlated with BedtimeHigh correlation
Bedtime is highly correlated with TST and 3 other fieldsHigh correlation
TST is highly correlated with Bedtime and 4 other fieldsHigh correlation
WASO is highly correlated with SMIHigh correlation
Waketime is highly correlated with OffsetHigh correlation
TIB is highly correlated with Bedtime and 3 other fieldsHigh correlation
NOA is highly correlated with AIHigh correlation
DSD is highly correlated with BS and 1 other fieldsHigh correlation
REMSD is highly correlated with SS and 2 other fieldsHigh correlation
SS is highly correlated with TST and 2 other fieldsHigh correlation
MS is highly correlated with REMSD and 2 other fieldsHigh correlation
BS is highly correlated with DSD and 1 other fieldsHigh correlation
SMI is highly correlated with WASOHigh correlation
AI is highly correlated with NOAHigh correlation
REMP is highly correlated with REMSD and 1 other fieldsHigh correlation
SWSP is highly correlated with DSD and 1 other fieldsHigh correlation
Onset is highly correlated with Bedtime and 3 other fieldsHigh correlation
Offset is highly correlated with WaketimeHigh correlation
TSDP is highly correlated with Bedtime and 3 other fieldsHigh correlation
IsWeekend is highly correlated with DayHigh correlation
Day is highly correlated with IsWeekendHigh correlation
df_index is highly correlated with Date and 2 other fieldsHigh correlation
Date is highly correlated with df_index and 26 other fieldsHigh correlation
Bedtime is highly correlated with Date and 15 other fieldsHigh correlation
SOL is highly correlated with Date and 8 other fieldsHigh correlation
TST is highly correlated with Date and 10 other fieldsHigh correlation
WASO is highly correlated with Date and 8 other fieldsHigh correlation
Waketime is highly correlated with Date and 2 other fieldsHigh correlation
TIB is highly correlated with Date and 8 other fieldsHigh correlation
NOA is highly correlated with Date and 7 other fieldsHigh correlation
LSD is highly correlated with Date and 10 other fieldsHigh correlation
DSD is highly correlated with Date and 4 other fieldsHigh correlation
REMSD is highly correlated with Date and 8 other fieldsHigh correlation
ARR is highly correlated with df_index and 6 other fieldsHigh correlation
SS is highly correlated with Date and 10 other fieldsHigh correlation
MS is highly correlated with Date and 9 other fieldsHigh correlation
BS is highly correlated with Date and 6 other fieldsHigh correlation
SE is highly correlated with Date and 11 other fieldsHigh correlation
SMI is highly correlated with Date and 9 other fieldsHigh correlation
AI is highly correlated with Date and 5 other fieldsHigh correlation
REMP is highly correlated with Date and 10 other fieldsHigh correlation
SWSP is highly correlated with Date and 10 other fieldsHigh correlation
Onset is highly correlated with Date and 14 other fieldsHigh correlation
Offset is highly correlated with Date and 2 other fieldsHigh correlation
TSDP is highly correlated with Date and 15 other fieldsHigh correlation
Midpoint is highly correlated with Date and 7 other fieldsHigh correlation
Day is highly correlated with Date and 2 other fieldsHigh correlation
IsWeekend is highly correlated with Date and 2 other fieldsHigh correlation
SleepRegularity is highly correlated with df_index and 10 other fieldsHigh correlation
Bedtime has 11 (26.2%) missing values Missing
SOL has 11 (26.2%) missing values Missing
TST has 11 (26.2%) missing values Missing
WASO has 11 (26.2%) missing values Missing
Waketime has 11 (26.2%) missing values Missing
TIB has 11 (26.2%) missing values Missing
NOA has 11 (26.2%) missing values Missing
LSD has 11 (26.2%) missing values Missing
DSD has 11 (26.2%) missing values Missing
REMSD has 11 (26.2%) missing values Missing
ARR has 11 (26.2%) missing values Missing
SS has 11 (26.2%) missing values Missing
MS has 11 (26.2%) missing values Missing
BS has 11 (26.2%) missing values Missing
SE has 11 (26.2%) missing values Missing
SMI has 11 (26.2%) missing values Missing
AI has 11 (26.2%) missing values Missing
REMP has 11 (26.2%) missing values Missing
SWSP has 11 (26.2%) missing values Missing
Onset has 11 (26.2%) missing values Missing
Offset has 11 (26.2%) missing values Missing
TSDP has 11 (26.2%) missing values Missing
Midpoint has 11 (26.2%) missing values Missing
SleepRegularity has 6 (14.3%) missing values Missing
df_index is uniformly distributed Uniform
Day is uniformly distributed Uniform
df_index has unique values Unique
Date has unique values Unique
df_index has 1 (2.4%) zeros Zeros

Reproduction

Analysis started2022-11-25 20:46:54.031176
Analysis finished2022-11-25 20:48:20.948049
Duration1 minute and 26.92 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE
ZEROS

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.5
Minimum0
Maximum41
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:21.091079image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.05
Q110.25
median20.5
Q330.75
95-th percentile38.95
Maximum41
Range41
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.26784415
Coefficient of variation (CV)0.5984314218
Kurtosis-1.2
Mean20.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum861
Variance150.5
MonotonicityNot monotonic
2022-11-25T20:48:21.244412image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
01
 
2.4%
211
 
2.4%
351
 
2.4%
361
 
2.4%
371
 
2.4%
381
 
2.4%
391
 
2.4%
401
 
2.4%
191
 
2.4%
201
 
2.4%
Other values (32)32
76.2%
ValueCountFrequency (%)
01
2.4%
11
2.4%
21
2.4%
31
2.4%
41
2.4%
51
2.4%
61
2.4%
71
2.4%
81
2.4%
91
2.4%
ValueCountFrequency (%)
411
2.4%
401
2.4%
391
2.4%
381
2.4%
371
2.4%
361
2.4%
351
2.4%
341
2.4%
331
2.4%
321
2.4%

Date
Date

HIGH CORRELATION
UNIQUE

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size464.0 B
Minimum2022-09-30 00:00:00
Maximum2022-11-10 00:00:00
2022-11-25T20:48:21.412648image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:21.628227image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

Bedtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean23.45537634
Minimum21.44611111
Maximum26.335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:21.819230image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum21.44611111
5-th percentile22.22916667
Q122.70347222
median23.37
Q324.08833334
95-th percentile24.71430555
Maximum26.335
Range4.88888889
Interquartile range (IQR)1.384861115

Descriptive statistics

Standard deviation1.005486463
Coefficient of variation (CV)0.04286805928
Kurtosis0.897388388
Mean23.45537634
Median Absolute Deviation (MAD)0.69777778
Skewness0.5368436484
Sum727.1166667
Variance1.011003028
MonotonicityNot monotonic
2022-11-25T20:48:22.003261image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
23.062777781
 
2.4%
23.9351
 
2.4%
23.836944441
 
2.4%
24.241666671
 
2.4%
22.672222221
 
2.4%
23.275833331
 
2.4%
24.673333331
 
2.4%
24.308888891
 
2.4%
24.493888891
 
2.4%
23.64751
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
21.446111111
2.4%
22.178611111
2.4%
22.279722221
2.4%
22.298888891
2.4%
22.337222221
2.4%
22.387222221
2.4%
22.571666671
2.4%
22.672222221
2.4%
22.734722221
2.4%
22.961388891
2.4%
ValueCountFrequency (%)
26.3351
2.4%
24.755277781
2.4%
24.673333331
2.4%
24.628611111
2.4%
24.50751
2.4%
24.493888891
2.4%
24.308888891
2.4%
24.241666671
2.4%
23.9351
2.4%
23.836944441
2.4%

SOL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)71.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean22.61290323
Minimum5
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:22.146702image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.5
Q110.5
median18
Q330.5
95-th percentile50
Maximum79
Range74
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.40869164
Coefficient of variation (CV)0.7256340097
Kurtosis3.389348346
Mean22.61290323
Median Absolute Deviation (MAD)10
Skewness1.634513516
Sum701
Variance269.2451613
MonotonicityNot monotonic
2022-11-25T20:48:22.329563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
85
11.9%
212
 
4.8%
182
 
4.8%
152
 
4.8%
52
 
4.8%
172
 
4.8%
361
 
2.4%
161
 
2.4%
341
 
2.4%
101
 
2.4%
Other values (12)12
28.6%
(Missing)11
26.2%
ValueCountFrequency (%)
52
 
4.8%
85
11.9%
101
 
2.4%
111
 
2.4%
131
 
2.4%
152
 
4.8%
161
 
2.4%
172
 
4.8%
182
 
4.8%
191
 
2.4%
ValueCountFrequency (%)
791
2.4%
511
2.4%
491
2.4%
401
2.4%
391
2.4%
361
2.4%
341
2.4%
331
2.4%
281
2.4%
271
2.4%

TST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)96.8%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean413.6774194
Minimum258
Maximum534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:22.484563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum258
5-th percentile353
Q1386
median417
Q3445
95-th percentile463.5
Maximum534
Range276
Interquartile range (IQR)59

Descriptive statistics

Standard deviation48.46606173
Coefficient of variation (CV)0.11715907
Kurtosis3.097415668
Mean413.6774194
Median Absolute Deviation (MAD)30
Skewness-0.6796162513
Sum12824
Variance2348.95914
MonotonicityNot monotonic
2022-11-25T20:48:22.615777image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4552
 
4.8%
3901
 
2.4%
3821
 
2.4%
3661
 
2.4%
4651
 
2.4%
4281
 
2.4%
3401
 
2.4%
3741
 
2.4%
3711
 
2.4%
4331
 
2.4%
Other values (20)20
47.6%
(Missing)11
26.2%
ValueCountFrequency (%)
2581
2.4%
3401
2.4%
3661
2.4%
3711
2.4%
3741
2.4%
3781
2.4%
3821
2.4%
3851
2.4%
3871
2.4%
3901
2.4%
ValueCountFrequency (%)
5341
2.4%
4651
2.4%
4621
2.4%
4611
2.4%
4552
4.8%
4541
2.4%
4471
2.4%
4431
2.4%
4391
2.4%
4331
2.4%

WASO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct23
Distinct (%)74.2%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean39.4516129
Minimum12
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:22.809778image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile18.5
Q129
median36
Q344
95-th percentile80
Maximum104
Range92
Interquartile range (IQR)15

Descriptive statistics

Standard deviation19.39216115
Coefficient of variation (CV)0.4915429236
Kurtosis4.309247072
Mean39.4516129
Median Absolute Deviation (MAD)8
Skewness1.828435033
Sum1223
Variance376.055914
MonotonicityNot monotonic
2022-11-25T20:48:22.948777image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
443
 
7.1%
312
 
4.8%
382
 
4.8%
362
 
4.8%
432
 
4.8%
332
 
4.8%
502
 
4.8%
321
 
2.4%
351
 
2.4%
261
 
2.4%
Other values (13)13
31.0%
(Missing)11
26.2%
ValueCountFrequency (%)
121
2.4%
181
2.4%
191
2.4%
211
2.4%
221
2.4%
241
2.4%
261
2.4%
271
2.4%
312
4.8%
321
2.4%
ValueCountFrequency (%)
1041
 
2.4%
911
 
2.4%
691
 
2.4%
502
4.8%
491
 
2.4%
443
7.1%
432
4.8%
411
 
2.4%
391
 
2.4%
382
4.8%

Waketime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean7.450268817
Minimum6.884722222
Maximum8.376666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:23.086055image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum6.884722222
5-th percentile6.960833334
Q17.17375
median7.312222222
Q37.625694445
95-th percentile8.212777778
Maximum8.376666667
Range1.491944445
Interquartile range (IQR)0.4519444445

Descriptive statistics

Standard deviation0.3877432895
Coefficient of variation (CV)0.05204420122
Kurtosis0.1634595444
Mean7.450268817
Median Absolute Deviation (MAD)0.193333334
Skewness0.9032532594
Sum230.9583333
Variance0.1503448585
MonotonicityNot monotonic
2022-11-25T20:48:23.228832image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5294444451
 
2.4%
8.3766666671
 
2.4%
7.1452777781
 
2.4%
7.3751
 
2.4%
7.5055555561
 
2.4%
7.3508333331
 
2.4%
7.9316666671
 
2.4%
7.2838888891
 
2.4%
7.6605555561
 
2.4%
7.7391666671
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
6.8847222221
2.4%
6.9416666671
2.4%
6.981
2.4%
7.1047222221
2.4%
7.1197222221
2.4%
7.1294444441
2.4%
7.1452777781
2.4%
7.1691666671
2.4%
7.1783333331
2.4%
7.2488888891
2.4%
ValueCountFrequency (%)
8.3766666671
2.4%
8.2702777781
2.4%
8.1552777781
2.4%
8.0686111111
2.4%
7.9316666671
2.4%
7.7597222221
2.4%
7.7391666671
2.4%
7.6605555561
2.4%
7.5908333331
2.4%
7.5766666671
2.4%

TIB
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)90.3%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean476.4193548
Minimum314
Maximum580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:23.403868image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum314
5-th percentile404
Q1445
median484
Q3518
95-th percentile538
Maximum580
Range266
Interquartile range (IQR)73

Descriptive statistics

Standard deviation53.80692285
Coefficient of variation (CV)0.1129402538
Kurtosis1.37299528
Mean476.4193548
Median Absolute Deviation (MAD)38
Skewness-0.7253491558
Sum14769
Variance2895.184946
MonotonicityNot monotonic
2022-11-25T20:48:23.588626image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4892
 
4.8%
4042
 
4.8%
5352
 
4.8%
5081
 
2.4%
4581
 
2.4%
4381
 
2.4%
4271
 
2.4%
5301
 
2.4%
4841
 
2.4%
4181
 
2.4%
Other values (18)18
42.9%
(Missing)11
26.2%
ValueCountFrequency (%)
3141
2.4%
4042
4.8%
4181
2.4%
4271
2.4%
4291
2.4%
4381
2.4%
4441
2.4%
4461
2.4%
4491
2.4%
4561
2.4%
ValueCountFrequency (%)
5801
2.4%
5391
2.4%
5371
2.4%
5352
4.8%
5301
2.4%
5221
2.4%
5191
2.4%
5171
2.4%
5081
2.4%
5061
2.4%

NOA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)22.6%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean5.774193548
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:23.715451image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median6
Q36.5
95-th percentile8.5
Maximum9
Range6
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.564388644
Coefficient of variation (CV)0.2709276422
Kurtosis0.1465747791
Mean5.774193548
Median Absolute Deviation (MAD)1
Skewness-0.1015147724
Sum179
Variance2.447311828
MonotonicityNot monotonic
2022-11-25T20:48:23.818452image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
613
31.0%
75
 
11.9%
34
 
9.5%
54
 
9.5%
92
 
4.8%
42
 
4.8%
81
 
2.4%
(Missing)11
26.2%
ValueCountFrequency (%)
34
 
9.5%
42
 
4.8%
54
 
9.5%
613
31.0%
75
 
11.9%
81
 
2.4%
92
 
4.8%
ValueCountFrequency (%)
92
 
4.8%
81
 
2.4%
75
 
11.9%
613
31.0%
54
 
9.5%
42
 
4.8%
34
 
9.5%

LSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)83.9%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean211.9677419
Minimum90
Maximum311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:23.956804image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile167
Q1192
median203
Q3233
95-th percentile283
Maximum311
Range221
Interquartile range (IQR)41

Descriptive statistics

Standard deviation42.21017561
Coefficient of variation (CV)0.1991349024
Kurtosis2.138503485
Mean211.9677419
Median Absolute Deviation (MAD)17
Skewness0.02062416585
Sum6571
Variance1781.698925
MonotonicityNot monotonic
2022-11-25T20:48:24.095250image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2202
 
4.8%
1922
 
4.8%
2612
 
4.8%
2032
 
4.8%
1932
 
4.8%
2141
 
2.4%
1961
 
2.4%
2321
 
2.4%
3111
 
2.4%
1861
 
2.4%
Other values (16)16
38.1%
(Missing)11
26.2%
ValueCountFrequency (%)
901
2.4%
1551
2.4%
1791
2.4%
1811
2.4%
1851
2.4%
1861
2.4%
1871
2.4%
1922
4.8%
1932
4.8%
1941
2.4%
ValueCountFrequency (%)
3111
2.4%
3051
2.4%
2612
4.8%
2531
2.4%
2521
2.4%
2461
2.4%
2341
2.4%
2321
2.4%
2202
4.8%
2181
2.4%

DSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)83.9%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean127.5483871
Minimum73
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:24.299978image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile79.5
Q1113.5
median126
Q3145.5
95-th percentile169
Maximum181
Range108
Interquartile range (IQR)32

Descriptive statistics

Standard deviation26.26256996
Coefficient of variation (CV)0.2059027993
Kurtosis-0.03296132138
Mean127.5483871
Median Absolute Deviation (MAD)15
Skewness-0.1015333899
Sum3954
Variance689.7225806
MonotonicityNot monotonic
2022-11-25T20:48:24.474986image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1112
 
4.8%
1262
 
4.8%
1182
 
4.8%
1472
 
4.8%
1642
 
4.8%
1521
 
2.4%
731
 
2.4%
791
 
2.4%
1051
 
2.4%
1241
 
2.4%
Other values (16)16
38.1%
(Missing)11
26.2%
ValueCountFrequency (%)
731
2.4%
791
2.4%
801
2.4%
951
2.4%
1051
2.4%
1112
4.8%
1121
2.4%
1151
2.4%
1161
2.4%
1182
4.8%
ValueCountFrequency (%)
1811
2.4%
1741
2.4%
1642
4.8%
1521
2.4%
1472
4.8%
1461
2.4%
1451
2.4%
1421
2.4%
1411
2.4%
1371
2.4%

REMSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)80.6%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean73.64516129
Minimum26
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:24.654096image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile29.5
Q155.5
median76
Q391
95-th percentile103.5
Maximum111
Range85
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation22.97469389
Coefficient of variation (CV)0.311964744
Kurtosis-0.4943088302
Mean73.64516129
Median Absolute Deviation (MAD)19
Skewness-0.5201840534
Sum2283
Variance527.8365591
MonotonicityNot monotonic
2022-11-25T20:48:24.778746image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
823
 
7.1%
522
 
4.8%
872
 
4.8%
752
 
4.8%
962
 
4.8%
891
 
2.4%
551
 
2.4%
281
 
2.4%
951
 
2.4%
681
 
2.4%
Other values (15)15
35.7%
(Missing)11
26.2%
ValueCountFrequency (%)
261
2.4%
281
2.4%
311
2.4%
471
2.4%
491
2.4%
522
4.8%
551
2.4%
561
2.4%
601
2.4%
681
2.4%
ValueCountFrequency (%)
1111
 
2.4%
1061
 
2.4%
1011
 
2.4%
991
 
2.4%
962
4.8%
951
 
2.4%
931
 
2.4%
891
 
2.4%
872
4.8%
823
7.1%

ARR
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct27
Distinct (%)87.1%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean13.90322581
Minimum13.25
Maximum14.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:24.968623image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum13.25
5-th percentile13.375
Q113.73
median13.88
Q314.125
95-th percentile14.315
Maximum14.85
Range1.6
Interquartile range (IQR)0.395

Descriptive statistics

Standard deviation0.3233819939
Coefficient of variation (CV)0.02325949376
Kurtosis1.394417418
Mean13.90322581
Median Absolute Deviation (MAD)0.2
Skewness0.4358516416
Sum431
Variance0.104575914
MonotonicityNot monotonic
2022-11-25T20:48:25.121617image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
13.753
 
7.1%
13.882
 
4.8%
14.152
 
4.8%
14.321
 
2.4%
13.591
 
2.4%
13.251
 
2.4%
14.081
 
2.4%
13.921
 
2.4%
14.141
 
2.4%
13.581
 
2.4%
Other values (17)17
40.5%
(Missing)11
26.2%
ValueCountFrequency (%)
13.251
 
2.4%
13.331
 
2.4%
13.421
 
2.4%
13.581
 
2.4%
13.591
 
2.4%
13.651
 
2.4%
13.681
 
2.4%
13.711
 
2.4%
13.753
7.1%
13.781
 
2.4%
ValueCountFrequency (%)
14.851
2.4%
14.321
2.4%
14.311
2.4%
14.241
2.4%
14.161
2.4%
14.152
4.8%
14.141
2.4%
14.111
2.4%
14.081
2.4%
14.071
2.4%

SS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct19
Distinct (%)61.3%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean84.4516129
Minimum59
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:25.272275image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile74.5
Q181
median85
Q390
95-th percentile92
Maximum94
Range35
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.15932357
Coefficient of variation (CV)0.08477426687
Kurtosis3.962486178
Mean84.4516129
Median Absolute Deviation (MAD)5
Skewness-1.551699421
Sum2618
Variance51.25591398
MonotonicityNot monotonic
2022-11-25T20:48:25.437276image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
904
 
9.5%
813
 
7.1%
913
 
7.1%
922
 
4.8%
792
 
4.8%
852
 
4.8%
882
 
4.8%
842
 
4.8%
591
 
2.4%
771
 
2.4%
Other values (9)9
21.4%
(Missing)11
26.2%
ValueCountFrequency (%)
591
 
2.4%
741
 
2.4%
751
 
2.4%
771
 
2.4%
792
4.8%
801
 
2.4%
813
7.1%
821
 
2.4%
831
 
2.4%
842
4.8%
ValueCountFrequency (%)
941
 
2.4%
922
4.8%
913
7.1%
904
9.5%
891
 
2.4%
882
4.8%
871
 
2.4%
861
 
2.4%
852
4.8%
842
4.8%

MS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct22
Distinct (%)71.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean77.90322581
Minimum50
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:25.596276image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile54
Q170
median82
Q388
95-th percentile91.5
Maximum94
Range44
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.1774514
Coefficient of variation (CV)0.1563151111
Kurtosis-0.2617418419
Mean77.90322581
Median Absolute Deviation (MAD)8
Skewness-0.8016639869
Sum2415
Variance148.2903226
MonotonicityNot monotonic
2022-11-25T20:48:25.723314image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
834
 
9.5%
903
 
7.1%
852
 
4.8%
822
 
4.8%
892
 
4.8%
712
 
4.8%
771
 
2.4%
731
 
2.4%
551
 
2.4%
781
 
2.4%
Other values (12)12
28.6%
(Missing)11
26.2%
ValueCountFrequency (%)
501
2.4%
531
2.4%
551
2.4%
621
2.4%
641
2.4%
661
2.4%
681
2.4%
691
2.4%
712
4.8%
731
2.4%
ValueCountFrequency (%)
941
 
2.4%
921
 
2.4%
911
 
2.4%
903
7.1%
892
4.8%
871
 
2.4%
852
4.8%
834
9.5%
822
4.8%
801
 
2.4%

BS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)54.8%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean86.41935484
Minimum72
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:25.847322image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile74
Q183.5
median87
Q390.5
95-th percentile94.5
Maximum96
Range24
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.965311914
Coefficient of variation (CV)0.06902749882
Kurtosis0.4696396685
Mean86.41935484
Median Absolute Deviation (MAD)4
Skewness-0.750382405
Sum2679
Variance35.58494624
MonotonicityNot monotonic
2022-11-25T20:48:25.966063image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
903
 
7.1%
913
 
7.1%
833
 
7.1%
863
 
7.1%
882
 
4.8%
872
 
4.8%
852
 
4.8%
842
 
4.8%
942
 
4.8%
742
 
4.8%
Other values (7)7
16.7%
(Missing)11
26.2%
ValueCountFrequency (%)
721
 
2.4%
742
4.8%
791
 
2.4%
821
 
2.4%
833
7.1%
842
4.8%
852
4.8%
863
7.1%
872
4.8%
882
4.8%
ValueCountFrequency (%)
961
 
2.4%
951
 
2.4%
942
4.8%
921
 
2.4%
913
7.1%
903
7.1%
891
 
2.4%
882
4.8%
872
4.8%
863
7.1%

SE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)96.8%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean86.84677419
Minimum77.08
Maximum93.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:26.150065image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum77.08
5-th percentile81.46
Q184.795
median87.21
Q389.33
95-th percentile91.56
Maximum93.56
Range16.48
Interquartile range (IQR)4.535

Descriptive statistics

Standard deviation3.461370044
Coefficient of variation (CV)0.03985605771
Kurtosis0.9439835773
Mean86.84677419
Median Absolute Deviation (MAD)2.17
Skewness-0.6479666027
Sum2692.25
Variance11.98108258
MonotonicityNot monotonic
2022-11-25T20:48:26.292099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
85.712
 
4.8%
89.571
 
2.4%
77.081
 
2.4%
87.211
 
2.4%
87.741
 
2.4%
88.431
 
2.4%
84.161
 
2.4%
89.471
 
2.4%
86.481
 
2.4%
89.281
 
2.4%
Other values (20)20
47.6%
(Missing)11
26.2%
ValueCountFrequency (%)
77.081
2.4%
81.031
2.4%
81.891
2.4%
82.171
2.4%
84.051
2.4%
84.161
2.4%
84.431
2.4%
84.541
2.4%
85.051
2.4%
85.691
2.4%
ValueCountFrequency (%)
93.561
2.4%
92.071
2.4%
91.051
2.4%
90.421
2.4%
89.781
2.4%
89.571
2.4%
89.471
2.4%
89.381
2.4%
89.281
2.4%
88.691
2.4%

SMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean90.55092779
Minimum78.23470411
Maximum95.37166895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:26.453099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum78.23470411
5-th percentile83.19596306
Q189.58853943
median90.86859693
Q392.64670054
95-th percentile95.1351124
Maximum95.37166895
Range17.13696484
Interquartile range (IQR)3.058161108

Descriptive statistics

Standard deviation3.854835057
Coefficient of variation (CV)0.04257090624
Kurtosis2.727709448
Mean90.55092779
Median Absolute Deviation (MAD)1.613998698
Skewness-1.445601743
Sum2807.078762
Variance14.85975332
MonotonicityNot monotonic
2022-11-25T20:48:26.590987image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
93.429158131
 
2.4%
78.234704111
 
2.4%
90.414201121
 
2.4%
92.893401061
 
2.4%
91.355599191
 
2.4%
90.200210711
 
2.4%
95.371668951
 
2.4%
90.447400251
 
2.4%
87.914691951
 
2.4%
90.680628271
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
78.234704111
2.4%
82.215743421
2.4%
84.17618271
2.4%
85.170340671
2.4%
87.914691951
2.4%
88.932419221
2.4%
89.0173411
2.4%
89.254598241
2.4%
89.922480621
2.4%
90.200210711
2.4%
ValueCountFrequency (%)
95.371668951
2.4%
95.145631061
2.4%
95.124593731
2.4%
95.094339631
2.4%
94.849023081
2.4%
93.835616411
2.4%
93.429158131
2.4%
92.893401061
2.4%
92.400000011
2.4%
91.951488411
2.4%

AI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)87.1%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean0.8458064516
Minimum0.43
Maximum1.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:26.748007image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.43
5-th percentile0.445
Q10.71
median0.86
Q31.005
95-th percentile1.165
Maximum1.27
Range0.84
Interquartile range (IQR)0.295

Descriptive statistics

Standard deviation0.2372449394
Coefficient of variation (CV)0.2804955424
Kurtosis-0.7747829353
Mean0.8458064516
Median Absolute Deviation (MAD)0.17
Skewness-0.2433467081
Sum26.22
Variance0.05628516129
MonotonicityNot monotonic
2022-11-25T20:48:26.883292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.793
 
7.1%
0.812
 
4.8%
1.122
 
4.8%
0.921
 
2.4%
0.981
 
2.4%
0.771
 
2.4%
0.531
 
2.4%
1.131
 
2.4%
0.971
 
2.4%
0.871
 
2.4%
Other values (17)17
40.5%
(Missing)11
26.2%
ValueCountFrequency (%)
0.431
2.4%
0.441
2.4%
0.451
2.4%
0.461
2.4%
0.531
2.4%
0.571
2.4%
0.651
2.4%
0.681
2.4%
0.741
2.4%
0.771
2.4%
ValueCountFrequency (%)
1.271
2.4%
1.171
2.4%
1.161
2.4%
1.131
2.4%
1.122
4.8%
1.091
2.4%
1.031
2.4%
0.981
2.4%
0.971
2.4%
0.951
2.4%

REMP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean17.87741935
Minimum5.63
Maximum25.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:27.029238image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5.63
5-th percentile6.78
Q115.34
median19.24
Q321.245
95-th percentile23.915
Maximum25.26
Range19.63
Interquartile range (IQR)5.905

Descriptive statistics

Standard deviation5.148553821
Coefficient of variation (CV)0.2879920037
Kurtosis0.4545793965
Mean17.87741935
Median Absolute Deviation (MAD)2.81
Skewness-1.004295243
Sum554.2
Variance26.50760645
MonotonicityNot monotonic
2022-11-25T20:48:27.181619image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
19.561
 
2.4%
12.051
 
2.4%
13.611
 
2.4%
16.391
 
2.4%
6.021
 
2.4%
22.21
 
2.4%
24.121
 
2.4%
20.051
 
2.4%
14.021
 
2.4%
18.941
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
5.631
2.4%
6.021
2.4%
7.541
2.4%
11.531
2.4%
12.051
2.4%
13.611
2.4%
14.021
2.4%
14.291
2.4%
16.391
2.4%
16.431
2.4%
ValueCountFrequency (%)
25.261
2.4%
24.121
2.4%
23.711
2.4%
23.011
2.4%
22.911
2.4%
22.21
2.4%
21.711
2.4%
21.671
2.4%
20.821
2.4%
20.791
2.4%

SWSP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean30.95870968
Minimum19.95
Maximum43.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:27.342046image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum19.95
5-th percentile20.96
Q127.26
median31.22
Q334.935
95-th percentile39.695
Maximum43.02
Range23.07
Interquartile range (IQR)7.675

Descriptive statistics

Standard deviation5.932860323
Coefficient of variation (CV)0.1916378423
Kurtosis-0.522979094
Mean30.95870968
Median Absolute Deviation (MAD)3.79
Skewness-0.1579839342
Sum959.72
Variance35.19883161
MonotonicityNot monotonic
2022-11-25T20:48:27.487540image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
33.411
 
2.4%
33.851
 
2.4%
37.171
 
2.4%
19.951
 
2.4%
27.11
 
2.4%
34.351
 
2.4%
23.241
 
2.4%
28.071
 
2.4%
33.421
 
2.4%
30.021
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
19.951
2.4%
20.671
2.4%
21.251
2.4%
22.11
2.4%
23.241
2.4%
24.31
2.4%
27.061
2.4%
27.11
2.4%
27.421
2.4%
28.071
2.4%
ValueCountFrequency (%)
43.021
2.4%
39.781
2.4%
39.611
2.4%
37.661
2.4%
37.171
2.4%
36.121
2.4%
35.011
2.4%
34.951
2.4%
34.921
2.4%
34.351
2.4%

Onset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean23.83225806
Minimum21.74611111
Maximum26.46833333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:27.628368image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum21.74611111
5-th percentile22.61680555
Q123.2175
median23.73333333
Q324.34736111
95-th percentile25.51430555
Maximum26.46833333
Range4.722222223
Interquartile range (IQR)1.129861112

Descriptive statistics

Standard deviation0.9749052999
Coefficient of variation (CV)0.04090696304
Kurtosis1.225926213
Mean23.83225806
Median Absolute Deviation (MAD)0.6588888933
Skewness0.6477040639
Sum738.8
Variance0.9504403438
MonotonicityNot monotonic
2022-11-25T20:48:27.778555image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
23.412777781
 
2.4%
24.068333331
 
2.4%
24.103611111
 
2.4%
24.808333341
 
2.4%
23.022222221
 
2.4%
23.44251
 
2.4%
25.991
 
2.4%
24.392222221
 
2.4%
24.627222221
 
2.4%
23.780833331
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
21.746111111
2.4%
22.529722221
2.4%
22.703888891
2.4%
22.871666671
2.4%
22.948888891
2.4%
22.995277781
2.4%
23.003888891
2.4%
23.022222221
2.4%
23.412777781
2.4%
23.428055561
2.4%
ValueCountFrequency (%)
26.468333331
2.4%
25.991
2.4%
25.038611111
2.4%
24.808333341
2.4%
24.711944441
2.4%
24.640833331
2.4%
24.627222221
2.4%
24.392222221
2.4%
24.30251
2.4%
24.103611111
2.4%

Offset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean7.450268817
Minimum6.884722222
Maximum8.376666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:27.924589image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum6.884722222
5-th percentile6.960833334
Q17.17375
median7.312222222
Q37.625694445
95-th percentile8.212777778
Maximum8.376666667
Range1.491944445
Interquartile range (IQR)0.4519444445

Descriptive statistics

Standard deviation0.3877432895
Coefficient of variation (CV)0.05204420122
Kurtosis0.1634595444
Mean7.450268817
Median Absolute Deviation (MAD)0.193333334
Skewness0.9032532594
Sum230.9583333
Variance0.1503448585
MonotonicityNot monotonic
2022-11-25T20:48:28.064263image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7.5294444451
 
2.4%
8.3766666671
 
2.4%
7.1452777781
 
2.4%
7.3751
 
2.4%
7.5055555561
 
2.4%
7.3508333331
 
2.4%
7.9316666671
 
2.4%
7.2838888891
 
2.4%
7.6605555561
 
2.4%
7.7391666671
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
6.8847222221
2.4%
6.9416666671
2.4%
6.981
2.4%
7.1047222221
2.4%
7.1197222221
2.4%
7.1294444441
2.4%
7.1452777781
2.4%
7.1691666671
2.4%
7.1783333331
2.4%
7.2488888891
2.4%
ValueCountFrequency (%)
8.3766666671
2.4%
8.2702777781
2.4%
8.1552777781
2.4%
8.0686111111
2.4%
7.9316666671
2.4%
7.7597222221
2.4%
7.7391666671
2.4%
7.6605555561
2.4%
7.5908333331
2.4%
7.5766666671
2.4%

TSDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean457.0806452
Minimum306.5
Maximum563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:28.219139image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum306.5
5-th percentile375.25
Q1424.7500001
median458.9999998
Q3496.7500001
95-th percentile515.5000001
Maximum563
Range256.5
Interquartile range (IQR)72

Descriptive statistics

Standard deviation52.36571664
Coefficient of variation (CV)0.1145655962
Kurtosis1.199058945
Mean457.0806452
Median Absolute Deviation (MAD)36.4999995
Skewness-0.7340562302
Sum14169.5
Variance2742.16828
MonotonicityNot monotonic
2022-11-25T20:48:28.369625image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
486.99999991
 
2.4%
498.51
 
2.4%
422.50000031
 
2.4%
393.99999981
 
2.4%
509.00000021
 
2.4%
474.50000021
 
2.4%
356.50000021
 
2.4%
413.49999991
 
2.4%
4221
 
2.4%
477.51
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
306.51
2.4%
356.50000021
2.4%
393.99999981
2.4%
397.51
2.4%
4121
2.4%
413.49999991
2.4%
4221
2.4%
422.50000031
2.4%
426.99999991
2.4%
432.50000021
2.4%
ValueCountFrequency (%)
5631
2.4%
516.50000011
2.4%
514.50000011
2.4%
510.49999991
2.4%
509.00000021
2.4%
499.99999991
2.4%
4991
2.4%
498.51
2.4%
495.00000011
2.4%
486.99999991
2.4%

Midpoint
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean4.104229391
Minimum3.604166669
Maximum5.0225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:28.516577image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.604166669
5-th percentile3.637222222
Q13.831527777
median4.058333332
Q34.247916667
95-th percentile4.826249999
Maximum5.0225
Range1.418333332
Interquartile range (IQR)0.4163888896

Descriptive statistics

Standard deviation0.3603743675
Coefficient of variation (CV)0.08780561055
Kurtosis0.7447314725
Mean4.104229391
Median Absolute Deviation (MAD)0.2202777763
Skewness0.9482394691
Sum127.2311111
Variance0.1298696848
MonotonicityNot monotonic
2022-11-25T20:48:28.658789image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4.0583333321
 
2.4%
4.22251
 
2.4%
3.6244444421
 
2.4%
4.0916666681
 
2.4%
4.2416666681
 
2.4%
3.9541666681
 
2.4%
4.9608333321
 
2.4%
3.8380555561
 
2.4%
4.143888891
 
2.4%
3.9791666671
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
3.6041666691
2.4%
3.6244444421
2.4%
3.6500000011
2.4%
3.7358333341
2.4%
3.7416666651
2.4%
3.76251
2.4%
3.8083333341
2.4%
3.8249999981
2.4%
3.8380555561
2.4%
3.8458333331
2.4%
ValueCountFrequency (%)
5.02251
2.4%
4.9608333321
2.4%
4.6916666671
2.4%
4.5969444461
2.4%
4.4911111111
2.4%
4.3041666681
2.4%
4.2875000011
2.4%
4.2541666651
2.4%
4.2416666681
2.4%
4.22251
2.4%

Day
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size464.0 B
Friday
Saturday
Sunday
Monday
Tuesday
Other values (2)
12 

Length

Max length9
Median length7
Mean length7.142857143
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowSaturday
3rd rowSunday
4th rowMonday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Friday6
14.3%
Saturday6
14.3%
Sunday6
14.3%
Monday6
14.3%
Tuesday6
14.3%
Wednesday6
14.3%
Thursday6
14.3%

Length

2022-11-25T20:48:28.821855image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-25T20:48:28.928859image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
friday6
14.3%
saturday6
14.3%
sunday6
14.3%
monday6
14.3%
tuesday6
14.3%
wednesday6
14.3%
thursday6
14.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IsWeekend
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size170.0 B
False
30 
True
12 
ValueCountFrequency (%)
False30
71.4%
True12
 
28.6%
2022-11-25T20:48:29.008856image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

SleepRegularity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct33
Distinct (%)91.7%
Missing6
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean0.3226947082
Minimum0.1422780931
Maximum0.6276847889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:48:29.110822image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.1422780931
5-th percentile0.1429319605
Q10.2021388054
median0.2552195965
Q30.4277021389
95-th percentile0.5893551095
Maximum0.6276847889
Range0.4854066958
Interquartile range (IQR)0.2255633334

Descriptive statistics

Standard deviation0.147405001
Coefficient of variation (CV)0.4567939827
Kurtosis-0.8438127856
Mean0.3226947082
Median Absolute Deviation (MAD)0.1045936256
Skewness0.6046761429
Sum11.6170095
Variance0.02172823431
MonotonicityNot monotonic
2022-11-25T20:48:29.258888image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.24100988352
 
4.8%
0.62768478892
 
4.8%
0.14227809312
 
4.8%
0.57657854971
 
2.4%
0.41381335671
 
2.4%
0.44408968451
 
2.4%
0.44517557711
 
2.4%
0.1930290421
 
2.4%
0.50995405711
 
2.4%
0.35233716741
 
2.4%
Other values (23)23
54.8%
(Missing)6
 
14.3%
ValueCountFrequency (%)
0.14227809312
4.8%
0.14314991621
2.4%
0.15925808241
2.4%
0.17604844671
2.4%
0.1930290421
2.4%
0.19510325011
2.4%
0.19548047481
2.4%
0.19781795881
2.4%
0.20357908771
2.4%
0.20431117411
2.4%
ValueCountFrequency (%)
0.62768478892
4.8%
0.57657854971
2.4%
0.55511598291
2.4%
0.50995405711
2.4%
0.49822863431
2.4%
0.44517557711
2.4%
0.44408968451
2.4%
0.43158409921
2.4%
0.42640815211
2.4%
0.42342150741
2.4%

Interactions

2022-11-25T20:48:15.761410image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:55.383170image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:58.823177image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:02.166172image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:06.195179image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:09.567990image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:13.085248image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:16.654928image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:20.608157image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:23.716159image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:27.008753image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:30.415506image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:33.298268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:36.579108image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:39.713828image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:43.025400image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:46.071131image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:49.402217image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:52.687775image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:55.717281image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:59.521918image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:02.558739image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:05.567005image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:08.824842image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:11.923558image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:15.887372image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:55.501171image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:58.964227image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:02.319172image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:06.322268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:09.703989image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:13.242284image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:16.790968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:20.782155image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:23.890111image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:27.149737image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:30.537507image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:33.435408image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:36.717348image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:39.832788image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:43.148390image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:46.222146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:49.531204image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:52.812781image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:55.851278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:47:59.641881image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:02.690694image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:05.695966image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:48:08.949416image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexDateBedtimeSOLTSTWASOWaketimeTIBNOALSDDSDREMSDARRSSMSBSSESMIAIREMPSWSPOnsetOffsetTSDPMidpointDayIsWeekendSleepRegularity
002022-09-3023.06277821.0455.032.07.529444508.06.0214.0152.089.013.8692.087.092.089.5793.4291580.7919.5633.4123.4127787.529444487.04.058333FridayTrueNaN
112022-10-0122.38722219.0461.050.07.312222535.09.0252.0112.096.013.8292.094.083.086.1789.2545981.1720.8224.3022.7038897.312222516.54.304167SaturdayTrueNaN
222022-10-0222.57166718.0447.031.06.980000504.06.0246.095.0106.014.1191.091.079.088.6991.8807810.8123.7121.2522.8716676.980000486.54.054167SundayFalse0.143150
332022-10-0322.73472251.0411.022.06.884722489.03.0253.0126.031.014.1579.053.087.084.0593.8356160.447.5430.6623.5847226.884722438.03.650000MondayFalse0.270951
442022-10-0424.5075008.0378.018.07.265833404.06.0185.0118.074.014.8585.083.085.093.5695.0943400.9519.5831.2224.6408337.265833397.53.953333TuesdayFalse0.236354
552022-10-0522.27972215.0423.091.07.104722522.04.0220.0116.087.013.9581.082.084.081.0382.2157430.5720.5727.4222.5297227.104722514.54.287500WednesdayFalse0.241010
6312022-10-06NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNThursdayFalse0.241010
7322022-10-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFridayTrue0.269429
862022-10-0823.13472233.0443.038.07.759722517.06.0206.0141.096.014.3190.089.090.085.6991.4344690.8121.6731.8323.6847227.759722484.54.037500SaturdayTrue0.230007
972022-10-0923.72416713.0421.036.07.590833471.06.0192.0147.081.014.2488.080.091.089.3891.7211330.8619.2434.9223.9408337.590833459.03.825000SundayFalse0.238326

Last rows

df_indexDateBedtimeSOLTSTWASOWaketimeTIBNOALSDDSDREMSDARRSSMSBSSESMIAIREMPSWSPOnsetOffsetTSDPMidpointDayIsWeekendSleepRegularity
32412022-11-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTuesdayFalse0.627685
33222022-11-0223.6475008.0433.044.07.739167485.07.0220.0130.082.013.6591.085.088.089.2890.6806280.9718.9430.0223.7808337.739167477.53.979167WednesdayFalse0.576579
34232022-11-0324.4938898.0371.050.07.660556429.07.0194.0124.052.013.5879.069.086.086.4887.9146921.1314.0233.4224.6272227.660556422.04.143889ThursdayFalse0.509954
35242022-11-0424.3088895.0374.033.07.283889418.07.0193.0105.075.014.1483.078.082.089.4790.4474001.1220.0528.0724.3922227.283889413.53.838056FridayTrue0.498229
36252022-11-0524.67333379.0340.012.07.931667404.03.0179.079.082.014.1575.090.074.084.1695.3716690.5324.1223.2425.9900007.931667356.54.960833SaturdayTrue0.555116
37262022-11-0623.27583310.0428.043.07.350833484.08.0186.0147.095.013.9290.089.091.088.4390.2002111.1222.2034.3523.4425007.350833474.53.954167SundayFalse0.431584
38272022-11-0722.67222221.0465.044.07.505556530.06.0311.0126.028.014.0885.055.087.087.7491.3555990.776.0227.1023.0222227.505556509.04.241667MondayFalse0.405667
39282022-11-0824.24166734.0366.026.07.375000427.06.0232.073.060.013.2577.073.072.085.7192.8934010.9816.3919.9524.8083337.375000394.04.091667TuesdayFalse0.372045
40292022-11-0923.83694416.0382.035.07.145278438.06.0187.0142.052.013.5981.064.090.087.2190.4142010.9413.6137.1724.1036117.145278422.53.624444WednesdayFalse0.423422
41302022-11-1023.11027827.0454.049.08.068611537.06.0203.0164.087.013.3389.082.094.084.5488.9324190.7919.1636.1223.5602788.068611510.54.254167ThursdayFalse0.426408